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Haußmann et al., 2020 - Google Patents

Sampling-free variational inference of bayesian neural networks by variance backpropagation

Haußmann et al., 2020

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Document ID
13679153164811417927
Author
Haußmann M
Hamprecht F
Kandemir M
Publication year
Publication venue
Uncertainty in Artificial Intelligence

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Snippet

We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ReLU nonlinearities into the product of an …
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    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
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